library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.2 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
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library(cowplot)
library(latex2exp)## Warning: package 'latex2exp' was built under R version 4.0.5library(plotly)##
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## layout library(gapminder)
library("png")
library("RCurl")## Warning: package 'RCurl' was built under R version 4.0.5##
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## completelibrary("gifski")
library("grDevices")
library("gganimate")
library("ggridges")#reading in data sets
countries_total <- read_csv("countries_total.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## name = col_character(),
## `alpha-2` = col_character(),
## `alpha-3` = col_character(),
## `country-code` = col_double(),
## `iso_3166-2` = col_character(),
## region = col_character(),
## `sub-region` = col_character(),
## `intermediate-region` = col_character(),
## `region-code` = col_double(),
## `sub-region-code` = col_double(),
## `intermediate-region-code` = col_double()
## )income_per_person <- read_csv("income_per_person.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## geo = col_character()
## )
## ℹ Use `spec()` for the full column specifications.life_expectancy_years <- read_csv("life_expectancy_years.csv")##
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## cols(
## .default = col_double(),
## geo = col_character()
## )
## ℹ Use `spec()` for the full column specifications.population_total <- read_csv("population_total.csv")##
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## cols(
## .default = col_double(),
## geo = col_character()
## )
## ℹ Use `spec()` for the full column specifications.Changing income_per_person data set to longitudinal data set.
#head(countries_total)
#head(population_total)
#head(life_expectancy_years)L_income_person <- income_per_person %>%
gather(key = "year", value = "Income",
2:220)
#head(L_income_person)#head(life_expectancy_years)#longitudinal data such that the resulting data set has three columns for the data sets required
L_life_expect <- life_expectancy_years %>%
gather(key = "year", value = "LifeExp",
2:220)
head(L_life_expect)## # A tibble: 6 x 3
## geo year LifeExp
## <chr> <chr> <dbl>
## 1 Afghanistan 1800 28.2
## 2 Albania 1800 35.4
## 3 Algeria 1800 28.8
## 4 Andorra 1800 NA
## 5 Angola 1800 27
## 6 Antigua and Barbuda 1800 33.5Merging the two longitudinal data sets.
Life_expect_income<- merge(L_income_person,L_life_expect,
by.x =
c("geo","year"),
by.y = c("geo","year"))head(Life_expect_income)## geo year Income LifeExp
## 1 Afghanistan 1800 603 28.2
## 2 Afghanistan 1801 603 28.2
## 3 Afghanistan 1802 603 28.2
## 4 Afghanistan 1803 603 28.2
## 5 Afghanistan 1804 603 28.2
## 6 Afghanistan 1805 603 28.2Merging life expeect and income with the countries data.
#Merging life expeect and income with the countries data.
head(countries_total)## # A tibble: 6 x 11
## name `alpha-2` `alpha-3` `country-code` `iso_3166-2` region `sub-region`
## <chr> <chr> <chr> <dbl> <chr> <chr> <chr>
## 1 "Afghani… AF AFG 4 ISO 3166-2:AF Asia Southern As…
## 2 "\xea\xf… AX ALA 248 ISO 3166-2:AX Europe Northern Eu…
## 3 "Albania" AL ALB 8 ISO 3166-2:AL Europe Southern Eu…
## 4 "Algeria" DZ DZA 12 ISO 3166-2:DZ Africa Northern Af…
## 5 "America… AS ASM 16 ISO 3166-2:AS Ocean… Polynesia
## 6 "Andorra" AD AND 20 ISO 3166-2:AD Europe Southern Eu…
## # … with 4 more variables: intermediate-region <chr>, region-code <dbl>,
## # sub-region-code <dbl>, intermediate-region-code <dbl>countries_total <- countries_total %>%
select(name,region)life_ex_income <- merge(Life_expect_income,countries_total, by.x = "geo",
by.y = "name")head(life_ex_income)## geo year Income LifeExp region
## 1 Afghanistan 1824 613 28.0 Asia
## 2 Afghanistan 1825 615 27.9 Asia
## 3 Afghanistan 1888 761 28.5 Asia
## 4 Afghanistan 1826 617 27.9 Asia
## 5 Afghanistan 1886 756 28.4 Asia
## 6 Afghanistan 1887 759 28.5 AsiaConverting pop_total data set to longitudinal dataset.
L_populationT <- population_total %>%
gather(key = "year", value = "Population",
2:220)
head(L_populationT)## # A tibble: 6 x 3
## geo year Population
## <chr> <chr> <dbl>
## 1 Afghanistan 1800 3280000
## 2 Albania 1800 410000
## 3 Algeria 1800 2500000
## 4 Andorra 1800 2650
## 5 Angola 1800 1570000
## 6 Antigua and Barbuda 1800 37000#Merging LifeExpIncome data set with population_total data set.
lifeE_geo_income <- merge(life_ex_income,L_populationT,
by.x= c("geo","year"),
by.y = c("geo","year"))
head(lifeE_geo_income)## geo year Income LifeExp region Population
## 1 Afghanistan 1800 603 28.2 Asia 3280000
## 2 Afghanistan 1801 603 28.2 Asia 3280000
## 3 Afghanistan 1802 603 28.2 Asia 3280000
## 4 Afghanistan 1803 603 28.2 Asia 3280000
## 5 Afghanistan 1804 603 28.2 Asia 3280000
## 6 Afghanistan 1805 603 28.2 Asia 3280000Renaming Columns of LifeExp.Income.Region.Pop data set and converting data set to a tibble.
lifeE_geo_income <- lifeE_geo_income %>% rename(Country=geo,Year=year,Region=region)
head(lifeE_geo_income)## Country Year Income LifeExp Region Population
## 1 Afghanistan 1800 603 28.2 Asia 3280000
## 2 Afghanistan 1801 603 28.2 Asia 3280000
## 3 Afghanistan 1802 603 28.2 Asia 3280000
## 4 Afghanistan 1803 603 28.2 Asia 3280000
## 5 Afghanistan 1804 603 28.2 Asia 3280000
## 6 Afghanistan 1805 603 28.2 Asia 3280000lifeE_geo_income <- as_tibble(lifeE_geo_income)
#chaning type here
data.class(lifeE_geo_income)## [1] "tbl_df"lifeE_geo_income_in2015 <- lifeE_geo_income %>% filter(Year=="2015")
head(lifeE_geo_income_in2015)## # A tibble: 6 x 6
## Country Year Income LifeExp Region Population
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 Afghanistan 2015 1750 57.9 Asia 33700000
## 2 Albania 2015 11000 77.6 Europe 2920000
## 3 Algeria 2015 13700 77.3 Africa 39900000
## 4 Andorra 2015 46600 82.5 Europe 78000
## 5 Angola 2015 6230 64 Africa 27900000
## 6 Antigua and Barbuda 2015 20100 77.2 Americas 99900plot_ly(
data = lifeE_geo_income_in2015,
x = ~LifeExp,
y = ~Income,
color = ~Region,
text = ~Country,
alpha = 0.5,
size = ~Population,
type = "scatter",
mode = "markers"
) %>%
layout(showlegend = FALSE)## Warning: `line.width` does not currently support multiple values.
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## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.lifeE_geo_income$Year <- as.numeric(lifeE_geo_income$Year)
head(lifeE_geo_income)## # A tibble: 6 x 6
## Country Year Income LifeExp Region Population
## <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 Afghanistan 1800 603 28.2 Asia 3280000
## 2 Afghanistan 1801 603 28.2 Asia 3280000
## 3 Afghanistan 1802 603 28.2 Asia 3280000
## 4 Afghanistan 1803 603 28.2 Asia 3280000
## 5 Afghanistan 1804 603 28.2 Asia 3280000
## 6 Afghanistan 1805 603 28.2 Asia 3280000p <- ggplot(lifeE_geo_income, aes(x = Income,
y=LifeExp,
size = Population,
colour = Region)) + #Use different colors for different regions.
geom_point(aes(size = Population, ids = Country ),
show.legend = FALSE,
alpha = 0.7) +
scale_color_viridis_d() +
scale_x_log10() +
labs(title = 'Year: {frame_time}',
x = "Life Expectancy",
y = "Income") +
transition_time(Year)+
ease_aes('linear')## Warning: Ignoring unknown aesthetics: ids##
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## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_point). animate(p, renderer = gifski_renderer()) # this command will pop-up a new graphic window showing the animation.## Warning: Removed 6 rows containing missing values (geom_point).
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